Banger paper from Google DeepMind and colleagues. (bookmark it) A model reads its entire KV cache on every generated token, even though it ends up attending to a tiny slice of it. In other words, if you ask about one detail from a 1M-token conversation the global attention layers re-read all of it, per token. The usual fix is to guess the relevant tokens first with cheap proxy scores, which still costs O(N) every step. Declarative Attention asks the model instead. The model declares where…